Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 13, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI.

Sedat Ozer1, Deanna L Langer, Xin Liu

  • 1Department of Electrical and Computer Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, Illinois 60616, USA. sozer1@iit.edu

Medical Physics
|May 7, 2010
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Prostate Imaging Standards for Screening Magnetic Resonance Imaging (PRISM): International Consensus Recommendations.

JAMA oncology·2026
Same author

Change in mammographic density as a potential predictor of cancer recurrence after breast conservation surgery and adjuvant endocrine therapy: results of the MEDICI study.

Breast cancer research : BCR·2026
Same author

Automated vs. manual segmentation for small renal mass imaging.

Canadian Urological Association journal = Journal de l'Association des urologues du Canada·2026
Same author

Diagnostic Performance of Biparametric versus Multiparametric Magnetic Resonance Imaging for Prostate Cancer Diagnosis: An Updated Systematic Review and Meta-analysis.

European urology·2026
Same author

Leveraging Clinical Text and Class Conditioning for 3D Prostate MRI Generation.

IEEE transactions on bio-medical engineering·2025
Same author

AI-Based Segmentation Reduces the Retrospective Miss Rate of Pancreatic Ductal Adenocarcinoma.

Journal of imaging informatics in medicine·2025

Automated multispectral MRI methods improve prostate cancer detection by combining T2, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI data. Supervised algorithms offer better segmentation than unsupervised methods, enhancing diagnostic accuracy.

Area of Science:

  • Medical Imaging
  • Oncology
  • Machine Learning

Background:

  • Magnetic resonance imaging (MRI) is a promising alternative to transrectal ultrasound for prostate cancer detection.
  • Fusing information from multispectral MRI is an active research area for improved localization.
  • Current studies often rely on human readers, introducing observer variability.

Purpose of the Study:

  • Develop automated methods to combine pharmacokinetic parameters from dynamic contrast-enhanced (DCE) MRI with quantitative T2 MRI and diffusion-weighted imaging (DWI).
  • Remove observer variability and achieve reproducible results through automated analysis.
  • Compare the performance of automated supervised and unsupervised methods for prostate cancer localization using multispectral MRI.

Main Methods:

More Related Videos

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

Related Experiment Videos

Last Updated: Jun 13, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

  • Utilized multispectral MRI data (T2, DWI, DCE-MRI) from 20 prostate cancer patients.
  • Employed large margin classifiers (SVMs, RVMs) for prostate cancer segmentation, comparing them to a previously developed unsupervised method.
  • Developed thresholding schemes for SVMs and RVMs, and applied a thresholding method to fully automate the unsupervised fuzzy Markov random fields approach.

    • Main Results:

    • A supervised machine learning method demonstrated superior performance compared to the unsupervised method.
    • No significant difference was found between support vector machine (SVM) and relevance vector machine (RVM) segmentation results.
    • Multispectral MRI significantly improved prostate cancer segmentation performance over single MR images, consistent with human reader studies.

    Conclusions:

    • Automated methods utilizing multispectral MRI enhance prostate cancer diagnosis and segmentation.
    • Multispectral MRI provides superior information for differentiating cancerous and normal prostate regions compared to single MRI techniques.
    • Supervised algorithms are a viable and effective alternative to unsupervised algorithms in automated prostate cancer detection methods.